Open-Data and Data Acquisition for Smart Cities and Urban Mobility Studies: Potential Approaches and Current Challenges
Keywords:
Isochrone Maps, Open-Data, Open-data Source, Smart Cities, Travel Time, Urban MobilityAbstract
The experience of urban users is shaped by cities—by their shapes, components, and how they function. An immense quantity of data is included in the process of how the city functions, how it affects its inhabitants, and how its residents view its components. Researchers need an extensive number of datasets on land use (type & quantification) and geometric dimensions of the built environment (3D, form, & pattern) to fully grasp this relationship. In addition to the need to collect data about users’ experience via using web-based/location-based surveys. The acquisition, exploration, and analysis of these datasets contributes to enabling a better understanding, operation, and monitoring of the city’s systems. Thus, facilitating the design, implementation, and operation of functional, efficient, and reliable smart cities. This paper focuses on transportation and mobility, and how can open-data sources be utilized for data acquisition for urban mobility studies. This highlights possible, simple, and accessible open-data acquisition tools for urban planners. It further outlines the limitations and challenges for data acquisition related to the global south context. The main aim is to explore the potential of integrating different open-data sources, web-based tools, and data analytics in defining travel time map and accessibility with respect to modality of mobility. It examines the accessibility, availability, and obtainability of data from these open-data sources (i.e., OpenStreetMap, Uber Movement, Jupyter Notebook) to be further used in urban studies, specifically in the context of the selected case study area. An exploratory approach is adopted to perform an analysis between the built environment and travel time during mobility, using Isochrone map acquired from open-data sources. The aim is to delineate an approach that could be adopted by urban planners who are not well acquainted with open-data sources, python scripts and codes. This approach could be utilized, modified, and replicated in further urban studies related to other regional contexts similar to the Egyptian context.
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